Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography



      Compared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort.


      Three hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments.


      ML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%–79.5%] vs. 54.8% [45.7%–63.8%]), LAD (79.3 [73.9–84.0] vs. 59.6 [53.5–65.6]), LCX (84.1 [76.0–90.3] vs. 63.7 [54.1–72.6]), proximal (81.5 [74.6–87.1] vs. 63.8 [55.9–71.2]), middle (81.2 [75.7–85.9] vs. 59.4 [53.0–65.6]) and distal stenosis location (67.4 [57.0–76.6] vs. 51.6 [41.1–62.0]).


      In a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.



      AUC (area under the receiver operating characteristics curve), cCTA (coronary CT angiography), CI (95% confidence interval), CT-FFR (fractional flow reserve from coronary CT angiography), FFR (fractional flow reserve), LAD (left anterior descending coronary artery), LCX (left circumflex coronary artery), ML (machine learning), NPV (negative predictive value), PPV (positive predictive value), RCA (right coronary artery)
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        • Koo B.K.
        • Erglis A.
        • Doh J.H.
        • et al.
        Diagnosis of ischemia-causing coronary stenoses by noninvasive fractional flow reserve computed from coronary computed tomographic angiograms. Results from the prospective multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained via Noninvasive Fractional Flow Reserve) study.
        J Am Coll Cardiol. 2011; 58: 1989-1997
        • Min J.K.
        • Leipsic J.
        • Pencina M.J.
        • et al.
        Diagnostic accuracy of fractional flow reserve from anatomic CT angiography.
        J Am Med Assoc. 2012; 308: 1237-1245
        • Norgaard B.L.
        • Leipsic J.
        • Gaur S.
        • et al.
        Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (Analysis of Coronary Blood Flow Using CT Angiography: next Steps).
        J Am Coll Cardiol. 2014; 63: 1145-1155
        • Coenen A.
        • Lubbers M.M.
        • Kurata A.
        • et al.
        Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm.
        Radiology. 2015; 274: 674-683
        • Renker M.
        • Schoepf U.J.
        • Wang R.
        • et al.
        Comparison of diagnostic value of a novel noninvasive coronary computed tomography angiography method versus standard coronary angiography for assessing fractional flow reserve.
        Am J Cardiol. 2014; 114: 1303-1308
        • Itu L.
        • Rapaka S.
        • Passerini T.
        • et al.
        A machine-learning approach for computation of fractional flow reserve from coronary computed tomography.
        J Appl Physiol. 1985; 121 (2016): 42-52
        • Coenen A.
        • Kim Y.H.
        • Kruk M.
        • et al.
        Diagnostic accuracy of a machine-learning approach to coronary computed tomographic angiography-based fractional flow reserve: result from the MACHINE consortium.
        Circ Cardiovasc Imaging. 2018; 11e007217
        • Cury R.C.
        • Abbara S.
        • Achenbach S.
        • et al.
        CAD-RADS(TM) coronary artery disease - reporting and data system. An expert consensus document of the society of cardiovascular computed tomography (SCCT), the American college of radiology (ACR) and the north American society for cardiovascular imaging (NASCI). Endorsed by the American college of cardiology.
        J Cardiovasc Comput Tomogr. 2016; 10: 269-281
        • Coenen A.
        • Lubbers M.M.
        • Kurata A.
        • et al.
        Coronary CT angiography derived fractional flow reserve: methodology and evaluation of a point of care algorithm.
        J Cardiovasc Comput Tomogr. 2016; 10: 105-113
        • Austen W.G.
        • Edwards J.E.
        • Frye R.L.
        • et al.
        A reporting system on patients evaluated for coronary artery disease. Report of the ad hoc committee for grading of coronary artery disease, council on cardiovascular surgery, American heart association.
        Circulation. 1975; 51: 5-40
        • DeLong E.R.
        • DeLong D.M.
        • Clarke-Pearson D.L.
        Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
        Biometrics. 1988; 44: 837-845
        • Ferencik M.
        • Nomura C.H.
        • Maurovich-Horvat P.
        • et al.
        Quantitative parameters of image quality in 64-slice computed tomography angiography of the coronary arteries.
        Eur J Radiol. 2006; 57: 373-379
        • Kang D.
        • Slomka P.J.
        • Nakazato R.
        • et al.
        Automated knowledge-based detection of nonobstructive and obstructive arterial lesions from coronary CT angiography.
        Med Phys. 2013; 40041912
        • de Graaf M.A.
        • Broersen A.
        • Ahmed W.
        • et al.
        Feasibility of an automated quantitative computed tomography angiography-derived risk score for risk stratification of patients with suspected coronary artery disease.
        Am J Cardiol. 2014; 113: 1947-1955
        • Yan R.T.
        • Miller J.M.
        • Rochitte C.E.
        • et al.
        Predictors of inaccurate coronary arterial stenosis assessment by CT angiography.
        JACC Cardiovasc Imaging. 2013; 6: 963-972
        • Cami E.
        • Tagami T.
        • Raff G.
        • et al.
        Assessment of lesion-specific ischemia using fractional flow reserve (FFR) profiles derived from coronary computed tomography angiography (FFRCT) and invasive pressure measurements (FFRINV): importance of the site of measurement and implications for patient referral for invasive coronary angiography and percutaneous coronary intervention.
        J Cardiovasc Comput Tomogr. 2018; 12: 480-492
        • Takagi H.
        • Ishikawa Y.
        • Orii M.
        • et al.
        Optimized interpretation of fractional flow reserve derived from computed tomography: comparison of three interpretation methods.
        J Cardiovasc Comput Tomogr. 2019; 13: 134-141
        • Chinnaiyan K.M.
        • Safian R.D.
        • Gallagher M.L.
        • et al.
        Clinical use of CT-derived fractional flow reserve in the emergency department.
        JACC Cardiovasc Imaging. 2020; 13: 452-461
        • Baumann S.
        • Renker M.
        • Schoepf U.J.
        • et al.
        Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry.
        Eur J Radiol. 2019; 119108657
        • Nous F.M.A.
        • Coenen A.
        • Boersma E.
        • et al.
        Comparison of the diagnostic performance of coronary computed tomography angiography-derived fractional flow reserve in patients with versus without diabetes mellitus (from the MACHINE consortium).
        Am J Cardiol. 2019; 123: 537-543
        • Tesche C.
        • Otani K.
        • De Cecco C.N.
        • et al.
        Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry.
        JACC Cardiovasc Imaging. 2020; 13: 760-770